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YIELD VARIABILITY, YIELD MONITORING
and
YIELD MAPPING
This section of the Precision Farming
course addresses issues about crop yield as it relates to site-specific
crop management, including the nature of yield variability, how it is
measured, and how such measurements may be used to aid decision-making by
growers and agricultural service producers.
Assigned Reading:
Please read Chapter 3, "Yield
Monitoring and Mapping," in The Precision Farming Guide for
Agriculturists by Deere & Company.
YIELD VARIABILITY
Precision agriculture requires integration
of three elements: 1) positioning capabilities (currently, global
positioning system or GPS) to know where equipment is located; 2)
real-time mechanisms for controlling nutrient, pesticide, seed, water, or
other crop production inputs; and 3) databases or sensors that provide
information needed to develop input response to site-specific conditions.
The technologies associated with requirements 1 and 2 are advanced
compared with the understanding necessary to meet requirement 3. Building
databases to quantify yield variability will improve the understanding of
how various stresses affect plant growth, development, or yield, and
ultimately lead to optimum site-specific prescriptions (Karlen et al,
1998).
There is evidence that variation in
nutrient elements in the soil is not the foremost factor affecting crop
yields. Precision farming advocates have devoted significant efforts to
applying fertilizers more selectively to soils based on yield potential
and soil test results. The goal is to obtain more efficient use of applied
fertilizer, to reduce any excess application that might cause
environmental insult, and to improve economics. It should be emphasized
that soil fertility needs, particularly nitrogen, are dependent on yield
level and/or the amount of rainfall that occurs during the growing season.
Variable rate application of fertilizer is based on expected yield, a
parameter that is often difficult to predict (Runge and Hons, 1998). In
northern Italy, corn yield variability could be explained by soil NO3
content at the beginning of the growing season, and by spatial differences
in soil carbon and nitrogen content (Marchetti et al, 1998).
Kitchen et al (1995) in Missouri
found that variable-rate N fertilizer application resulted in lower
residual soil nitrate, but showed little economic benefit. Ferguson et
al (1995) in Nebraska detected no significant difference in corn grain
yield or use efficiency between variable rate and single rate fertilizer
application. Logsdon et al (1998) observed that crop yield
variability was influenced by stored soil water in rain-fed agriculture.
In a drier year soil water storage correlated with both corn and soybean
yields. In a wet year, predicted aeration stress correlated with corn
yields but not soybean yields.
Runge and Hons (1998) considered crop yield
variations associated with both temporal and spatial factors that they had
either experienced or obtained from the literature. Their intent was to
focus sources of yield variation into three primary categories: water,
fertility-management and genetics. They concluded that plant available
stored soil water and seasonal precipitation quantity and distribution had
the greatest effect on rainfed crop yields. To have a successful crop,
producers know that a uniform stand at the optimum population density,
adequate soil fertility and weed control are important. Farmers
characteristically refer to areas of good and poor soils that are most
often related to the variation in plant available stored water.
In a 16-ha field in Iowa, Karlen et al measured
spatial variability in corn emergence, growth, and yield as well as in
insect pressure and in several soil characteristics. Analysis of these
data led them to conclude that in regard to developing site-specific
management strategies, attention to soil water factors (drainage,
prevention of runoff), soil acidity, and erosion prevention are more
important than efforts to differentially apply fertilizer nutrients.
Variation in soil fertility and hydrologic
properties across landscapes affects crop yield. Nolan et al (1998) showed
that a simple landscape classification delimited areas within a rolling
field of canola that responded differently to N fertilizer and that
varying N fertilizer according to these landscape classes can increase
profits from $5 to $7/ha compared with uniform application of N. Five
landscape classes were recognizedžupper level, shoulderslope, backslope,
footslope, and lower level. Similarly, Khakural et al (1998) reported that
corn and soybean yields were less at eroded slopes that an nearly level
summits or at the foot/toeslope positions. Corn yield was positively
correlated with A horizon thickness and negatively correlated with surface
pH. A horizon thickness, surface pH, tillage system, and growing season
precipitation explained 72% of the variability in corn yield.
Greater crop yields were obtained in
footslope positions compared to the backslope and sideslope positions in
western Iowa (Spomer and Piest, 1982) and west central Minnesota (Khakural
et al, 1996).
YIELD MONITORING AND
MAPPING
The assigned reading in The Precision
Farming Guide for Agriculturists provides perspective on this topic by
contrasting traditional whole-field yield records with the information
provided by modern yield monitors. Various ways of measuring yield are
also reviewed.
Early efforts to develop instantaneous
yield monitors were concentrated on combine-harvested crops, perhaps
because the relatively steady flow of grain into the harvester seemed to
lend itself readily to measurement. Typical components of grain yield
monitoring systems include grain flow sensors, grain moisture sensors,
ground speed sensors, and a computer/display console.
Campbell (1998) has reviewed the recent
advances in yield monitoring of conveyor-harvested crops. In general, as
explained by Hall et al (1998), instantaneous yield for such crops is
calculated by the equation
where:
Yield = crop yield (tons per acre)
FR = flow rate (pounds per second)
GS = ground speed (miles per hour)
W = harvester width (feet)
The constant in the numerator is specific
for the units specified for the variables, and would, of course, be
different if other units were used, for example, pounds per acre for flow
rate, or if metric units are used for all variables. Essentially the same
equation is suitable for computing instantaneous yields of
combine-harvested crops, for which flow rate might be expressed in units
of either mass or volume per unit time.
These pieces of information are gathered by
yield monitors consisting of four basic components-product flow sensors,
speed sensors, signal conditioning and conversion units, and a computer.
The information is spatially referred with position data provided by a
differentially corrected Global Positioning System (DGPS).
Yield monitors continue to be improved for
use during the mechanized harvest of many crops, including
conveyor-harvested crops, such as potatoes, tomatoes, and sugar beets
(Campbell, 1998; Hall et al, 1998; Pelletier and Upadhyaya, 1998), peanut
(Durrence et al, 1998), cotton (Searcy, 1998; Perry et al, 1998; Gvili,
1998), rice (Iida et al, 1998) and other combine-harvested crops,
including wheat, corn, and soybeans (Sadler et al, 1998).
Yield monitoring and mapping is also being
explored for crops that are harvested by hand labor rather than mechanized
harvesters. For example, the yield of various fruits that are hand picked
might be monitored by recording weight increases for picking boxes or bins
and using DPGS to spatially reference each recorded weight. Perhaps this
could be facilitated by using bins identified by bar codes and a forklift
equipped with scales on its forks, DGPS receiver, and a bar code scanner.
This would also facilitate tracking of bins of fruit from specific point
of harvest as they proceed through the packing house.
FOR FURTHER INFORMATION
Students are urged to discover further
information about yield monitoring from the following websites:
http://www.harvestmaster.com/hm500.html
http:/www.deere.com/deerecom/Farmers+and+Ranchers/
Precision+Farming/GreenStar_TM+Combine+Systems/ http://www.casecorp.com/agricultural/afs.components_displays.html
http://www.agleader.com/products.htm
http://www.farmscan.net.au/
http://www.newholland.com/na/pfs/AWYM.html
http://www.rdstechnology.ltd.uk/pfsys.htm
Also, the University of Georgia has an
excellent website on yield mapping information resources at:
http://nespal.cpes.peachnet.edu/home/links/pa/
STUDY QUESTIONS
1. What soil characteristics seem to be
spatially correlated with crop yield?
2. Discuss the inherent difficulty in
getting good results from variable rate application of fertilizers.
3. What is crop yield? In what units is it
usually expressed for these crops?
grain
cotton
tomatoes
potatoes
sugar beets
baled hay
grapes
4. What are three different approaches to
measuring grain yield that have been used over the years?
5. What are the basic components of an
instantaneous yield monitor?
6. What are some of the methods of
measuring the flow rate of a crop commodity into a harvesting machine?
7. Explain in simple terms how each of
these methods of measuring flow rate works.
8. Why is a grain moisture sensor included
as part of a yield monitor for grain?
9. Describe briefly the type of sensor that
is most often used to measure grain moisture content.
10. Why is ground speed of the harvesting
machine needed information for the calculation of instantaneous crop
yield?
11. Discuss three alternative ways of
sensing ground speed.
12. Explain the role of a header position
sensor on a combine harvester for grain.
13. What are some software features of
yield monitoring systems that are aimed at making yield maps as accurate
as possible even in field headlands where harvesting machines turn around
and change direction?
14. What various functions may be performed
by the yield monitor's display console located for easy use by the
operator of the harvester?
15. Why is calibration of a yield
monitoring system necessary and, in general terms, how might calibration be
accomplished?
16. How can data be transferred from the
yield monitor console to a desktop computer?
17. What are some possible sources of
errors in the data provided by yield monitors?
18. Besides the crops mentioned in the
textbook or this website, are you aware of any other crops for which yield
monitors are being, or have been, developed? If so, what are those crops
and how did you learn about them?
Literature Cited
Campbell, R. H. 1998. Recent advances in
yield monitoring of conveyor harvested crops. p. 1101-1106. In P. C.
Robert et al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture.
American Society of Agronomy. Madison, WI.
Durrence, J. S., C. D. Perry, G. Vellidis,
D. L. Thomas, and C. K. Kvien. 1998. Mapping peanut yield variability with
an experimental load cell yield monitoring system. In P. C. Robert et al.
(ed.) Proc. 4th Int. Conf. on Precision Agriculture. American Society of
Agronomy. Madison, WI.
Ferguson, R. B., J. E. Cahoon, G. W.
Hergert, T. A. Peterson, C. A. Gotway, and A. H. Hartford. 1994. Managing
spatial variability with furrow irrigation to increase nitrogen use
efficiency. p. 443-464. In P. C. Robert et al. (ed.) Site-specific
management for agricultural systems. American Society of Agronomy,
Madison, WI.
Gvili, M. 1998. Cotton yield sensor
produces yield map. p. 1263. In P. C. Robert et al. (ed.) Proc. 4th Int.
Conf. on Precision Agriculture. American Society of Agronomy. Madison, WI.
Hall, T. L., L. F. Backer, V. L. Hofman,
and L. J. Smith. 1998. Evaluation of sugarbeet yield sensing systems
operating concurrently on a harvester. p. 1107-1118. In P. C. Robert et
al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture. American Society
of Agronomy. Madison, WI.
Iida, M., T. Kaho, C. K. Lee, M. Umeda, and
M. Suguri. 1998. Measurement of grain yields in Japanese paddy fields. p.
1165-1175. In P. C. Robert et al. (ed.) Proc. 4th Int. Conf. on Precision
Agriculture. American Society of Agronomy. Madison, WI.
Johnson, R. M., J. B. Bradow, P. J. Bauer,
and E. J. Sadler. 1998. Spatial variability of cotton fiber yield and
quality in relation to soil variability. p. 487-497. In P. C. Robert et
al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture. American Society
of Agronomy. Madison, WI.
Karlen, D. L., S. S. Andrews, T. S. Colvin,
D. B. Jaynes and E. C. Berry. 1998. Spatial and temporal variability in
corn growth, development, insect pressure, and yield. p. 101-112. In P. C.
Robert et al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture.
American Society of Agronomy. Madison, WI.
Khakural, B. R., P. C. Robert, and D. R.
Huggins. 1998. Variability of corn/soybean yield and soil/landscape
properties across a southwestern Minnesota landscape. p. 573-579. In P. C.
Robert et al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture.
American Society of Agronomy. Madison, WI.
Khakural, B. R., P. C. Robert, and D. J.
Mulla. 1996. Relating corn/soybean yield to variability in soil and
landscape characteristics. In P. C. Robert et al. (ed.) Proc. 3rd Int.
Conf. on Precision Agriculture. American Society of Agronomy. Madison, WI.
Kitchen, N. R., D. F. Hughes, K. A. Sudduth,
and S. J. Birrell. 1994. Comparison of variable rate to single rate
nitrogen fertilizer application: corn production and residual NO3-N. p.
427-439. In P. C. Robert et al. (ed.) Site-specific management for
agricultural systems. American Society of Agronomy, Madison, WI.
Logsdon, S., J. Proger, D. Meek, T. Colvin,
D. James and M. Milner. 1998. Crop yield variability as influenced by
water in rain-fed agriculture. p. 453-465. In P. C. Robert et al. (ed.)
Proc. 4th Int. Conf. on Precision Agriculture. American Society of
Agronomy. Madison, WI.
Marchetti, R., P. Spallacci, E. Ceotto, and
R. Papin. 1998. Predicting yield variability for corn grown in a silty-clay
soil in northern Italy. p. 467-478. In P. C. Robert et al. (ed.) Proc. 4th
Int. Conf. on Precision Agriculture. American Society of Agronomy.
Madison, WI.
Nolan, S. C., T. W. Goddard, D. C. Penney,
and F. M. Green. 1998. Yield response to nitrogen within landscape
classes. p. 479-485. In P. C. Robert et al. (ed.) Proc. 4th Int. Conf. on
Precision Agriculture. American Society of Agronomy. Madison, WI.
Pelletier, M. G., and S. K. Upadhyaya.
1998. Development of a tomato yield monitor. p. 1119-1129. In P. C. Robert
et al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture. American
Society of Agronomy. Madison, WI.
Perry, C. D., J. S. Durrence, D. L. Thomas,
G. Vellidis, C. J. Sobolik, and A. Dzubak. 1998. p. 1227-1240. In P. C.
Robert et al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture.
American Society of Agronomy. Madison, WI.
Runge, E. C. A., and F. H. Hons. 1998.
Precision Agriculture: development of a hierarchy of variables influencing
crop yields. p. 143-158. In P. C. Robert et al. (ed.) Proc. 4th Int. Conf.
on Precision Agriculture. American Society of Agronomy. Madison, WI.
Sadler, J., J. Millen, P. Fussell, J.
Spencer, and W. Spencer. 1998. Yield mapping of on-farm cooperative fields
in the southeast coastal plain. p. 1767-1776. In P. C. Robert et al. (ed.)
Proc. 4th Int. Conf. on Precision Agriculture. American Society of
Agronomy. Madison, WI.
Searcy, S. W. 1998. Evaluation of weighing
and flow-based cotton yield mapping techniques. p. 1151-1163. In P. C.
Robert et al. (ed.) Proc. 4th Int. Conf. on Precision Agriculture.
American Society of Agronomy. Madison, WI.
Spomer, R. G., and R. F. Piest. 1982. Soil
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